XindaL
Members
First name (team leader)
Chuhan
Last name
Lu
Organisation name
Nanjing University of Information Science and Technology
Organisation type
Research Organisation (Academic, Independent, etc.)
Organisation location
China
First name
Yichen
Last name
Shen
Organisation name
Nanjing University of Information Science and Technology
Organisation type
Academic (Student)
Organisation location
China
First name
Haonan
Last name
Ji
Organisation name
Nanjing University of Information Science and Technology
Organisation type
Academic (Student)
Organisation location
China
First name
Hao
Last name
Wu
Organisation name
Nanjing University of Information Science and Technology
Organisation type
Academic (Student)
Organisation location
China
First name
Jiawei
Last name
Zhang
Organisation name
Nanjing University of Information Science and Technology
Organisation type
Academic (Student)
Organisation location
China
Models
Please note that the model information below reflects the latest details provided by the team and does not show previous versions. Only the model summary questionnaires retain a submission history.
Model name
ISOX
Model type
Number of individuals supporting model development:
1-5
Maximum number of Central Processing Units (CPUs) supporting model development or forecast production:
< 8
Maximum number of Graphics Processing Units (GPUs) supporting model development or forecast production:
< 4
How would you best classify the IT system used for model development or forecast production:
Single node system
Model summary questionnaire for model ISOX
The list below shows all model summary questionnaires submitted for this model. Participants may submit an updated questionnaire whenever their methodology changes.
Questionnaires are displayed from the most recent to the earliest, with the latest version opened by default.
Which of the following descriptions best represent the overarching design of your forecasting model?
- Machine learning-based weather prediction.
What techniques did you use to initialise your model? (For example: data sources and processing of initial conditions)
We use NCEP-FNL reanalysis data to initialize our model.
If any, what data does your model rely on for real-time forecasting purposes?
We use NCEP-FNL reanalysis data as it has a relatively short release lag.
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
We use a reanalysis dataset.
Please provide an overview of your final ML/AI model architecture (For example: key design features, specific algorithms or frameworks used, and any pre- or post-processing steps)
This model combines the U-Net architecture and Transformer blocks.
Have you published or presented any work related to this forecasting model? If yes, could you share references or links?
Yes. Please refer to https://doi.org/10.1038/s41612-025-00902-7.
Before submitting your forecasts to the AI Weather Quest, did you validate your model against observational or independent datasets? If so, how?
Yes. You may refer to https://doi.org/10.1038/s41612-025-00902-7, where we tested our model against NCEP-CFS in forecasting ISO components and outperformed that model.
Did you face any challenges during model development, and how did you address them?
The biggest challenge for us is the lack of computation resources. We still cannot address it due to lack of research funding.
Are there any limitations to your current model that you aim to address in future iterations?
Probably no. But we are waiting for new neural network backbones coming out so we can try different kind of architectures.
Are there any other AI/ML model components or innovations that you wish to highlight?
No answer.
Who contributed to the development of this model? Please list all individuals who contributed to this model, along with their specific roles (e.g., data preparation, model architecture, model validation, etc) to acknowledge individual contributions.
Chuhan Lu – Computational Resources, Conceptualization
Yichen Shen – Model Architecture, Model Validation
Haonan Ji – Data Preparation
Hao Wu, Jiawei Zhang – Model Validation
Shnegwang Yang – Forecast Routine
Model name
ABC
Model type
Number of individuals supporting model development:
1-5
Maximum number of Central Processing Units (CPUs) supporting model development or forecast production:
< 8
Maximum number of Graphics Processing Units (GPUs) supporting model development or forecast production:
< 4
How would you best classify the IT system used for model development or forecast production:
Single node system
Submitted forecast data in previous period(s)
Please note: Submitted forecast data is only publicly available once the evaluation of a full competitive period has been completed. See the competition's full detailed schedule with submitted data publication dates for each period here.
Access forecasts data